---
title: "Parallel Computing in Pattern Causality Analysis"
author: "Stavros Stavroglou, Athanasios Pantelous, Hui Wang"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Parallel Computing in Pattern Causality}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  warning = FALSE,
  collapse = TRUE,
  comment = "#>",
  fig.width = 8,
  fig.height = 6
)
```

Pattern causality analysis involves computationally intensive tasks, especially when dealing with complex systems and large datasets. This vignette demonstrates how to leverage parallel computing capabilities in the `patterncausality` package to significantly reduce computation time and improve efficiency.

## Key Benefits of Parallel Computing

The parallel computing features in this package are particularly effective for:

1. **Bootstrap Analysis**:
   - Distributing bootstrap iterations across multiple cores
   - Ideal for uncertainty quantification
   - Significant speed improvements for large numbers of iterations

2. **Matrix Computations**:
   - Processing large causality matrices efficiently
   - Handling multiple time series simultaneously
   - Reducing computation time for system-wide analyses

3. **Cross-validation Studies**:
   - Parallel processing of different sample sizes
   - Efficient handling of repeated computations
   - Improved performance for robustness analysis

## Performance Comparison: Sequential vs Parallel Computing

Let's explore how parallel computing can enhance the performance of different pattern causality analyses:

```{r message = FALSE}
library(patterncausality)
data(climate_indices)
```

## Create test data

```{r}
X <- climate_indices$PNA
Y <- climate_indices$NAO
```

## Function to measure execution time

```{r}
run_cv_test <- function(n_cores) {
    start_time <- Sys.time()
    result <- pcCrossValidation(
        X = X,
        Y = Y,
        numberset = c(100, 200, 300, 400, 500),
        E = 3,
        tau = 2,
        metric = "euclidean",
        h = 1,
        weighted = FALSE,
        random = TRUE,
        bootstrap = 100,
        n_cores = n_cores,
        verbose = TRUE
    )
    end_time <- Sys.time()
    return(difftime(end_time, start_time, units = "secs"))
}
```


# Compare sequential vs parallel

```r
time_seq <- run_cv_test(1)
time_par <- run_cv_test(parallel::detectCores() - 1)

cat("Sequential computation time:", time_seq, "seconds\n")
cat("Parallel computation time:", time_par, "seconds\n")
cat("Speed-up factor:", as.numeric(time_seq) / as.numeric(time_par), "x\n")
```

## Matrix Analysis with Multiple Time Series

When analyzing causality between multiple time series, parallel computing can significantly reduce computation time:

```r
# Create larger test dataset
n_series <- 20
n_points <- 1000
test_data <- matrix(rnorm(n_series * n_points), ncol = n_series)
colnames(test_data) <- paste0("Series_", 1:n_series)

# Function to measure execution time
run_matrix_test <- function(n_cores) {
  start_time <- Sys.time()

  result <- pcMatrix(
    dataset = test_data,
    E = 3,
    tau = 2,
    metric = "euclidean",
    h = 1,
    weighted = FALSE,
    n_cores = n_cores,
    verbose = TRUE
  )

  end_time <- Sys.time()
  return(difftime(end_time, start_time, units = "secs"))
}

# Compare sequential vs parallel
time_seq <- run_matrix_test(1)
time_par <- run_matrix_test(parallel::detectCores() - 1)

cat("Sequential computation time:", time_seq, "seconds\n")
cat("Parallel computation time:", time_par, "seconds\n")
cat("Speed-up factor:", as.numeric(time_seq) / as.numeric(time_par), "x\n")
```

## Understanding Parallel Performance

### Key Factors Affecting Speed-up

1. **Data Characteristics**
   - Size of time series
   - Number of series
   - Sample sizes in cross-validation
   - Number of bootstrap iterations

2. **Hardware Considerations**
   - Number of CPU cores
   - Available memory
   - System architecture (Windows/Linux/Mac)

3. **Analysis Type**
   - Bootstrap analysis: Excellent parallelization potential
   - Matrix computation: Good for large matrices
   - Cross-validation: Depends on sample sizes

### Best Practices for Optimal Performance

```r
# Get available cores
n_cores <- parallel::detectCores()
# Use n_cores - 1 for computation
recommended_cores <- max(1, n_cores - 1)
cat("Recommended number of cores:", recommended_cores, "\n")

# Example of memory-efficient parallel computation
result <- pcCrossValidation(
  X = X,
  Y = Y,
  numberset = c(100, 200, 300),
  E = 3,
  tau = 2,
  bootstrap = 50,
  n_cores = 2,  # Use modest number of cores for memory efficiency
  verbose = TRUE
)
```

## System-Specific Considerations

### Windows Systems
- Uses PSOCK clusters
- Slightly higher overhead
- Consider using fewer cores

### Linux/Mac Systems
- Uses FORK clusters
- Better parallel performance
- Can utilize more cores effectively

### Memory Usage Guidelines
- Monitor system memory during computation
- Reduce core count if memory pressure is high
- Consider batch processing for very large datasets

## Conclusion

Parallel computing in pattern causality analysis can provide significant performance improvements, especially for:
- Large-scale bootstrap analysis
- Multi-series causality matrices
- Extensive cross-validation studies

Choose parallel computing parameters based on:
- Your system capabilities
- Dataset characteristics
- Analysis requirements
- Available computational resources

For optimal results, always monitor system performance and adjust parameters accordingly.

